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        Note that additional data was saved in multiqc_data when this report was generated.

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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.22.2

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2025-01-17, 02:40 UTC based on data in: /data/output/homology_networks/pggb_v1

        ODGI

        ODGI is an optimized dynamic graph/genome implementation, for efficient analysis and manipulation of pangenome graphs structured in the variation graph model.

        Detailed ODGI stats table.

        Showing 2/2 rows and 10/14 columns.
        Sample NameLengthNodesEdgesPathsComponentsACTGN
        seqwish
        2206574
        116710
        163038
        4016
        595
        770779
        332110
        772756
        330888
        19
        smooth
        2635922
        136515
        187517
        4016
        704
        920651
        417705
        927539
        369986
        41

        Sum of path node distances

        For each path we iterate from node to node and count the node / nucleotide distance of nodes on the pangenome level normalized by the path length. If a node is reversed, we count the node distance twice.

        This value allows you to evaluate the sorting goodness - how linear the graph is.

        Created with MultiQC

        For each path we iterate from node to node and count the node / nucleotide distance mean_links_length of nodes within the same path only. We then normalized by the path length.

        This value allows you to evaluate the sorting goodness - how linear the graph is.

        MultiQC v1.22.2 - Written by Phil Ewels, available on GitHub.

        This report uses Plotly, jQuery, jQuery UI, Bootstrap, FileSaver.js and clipboard.js.